An instantaneous torque testing method and related system for an aeroengine

Abstract

The present invention discloses an instantaneous torque testing method and related system for an aero-engine. The method includes: dividing the engine shafting into three temperature zones and applying acoustic excitation signals with different frequencies to obtain acoustic response signals; determining the operating phase points according to the rotational speed change rate and collecting torque response signals; collecting inter-axis crosstalk signals to extract the response time difference and amplitude ratio; collecting pressure field information to extract spatial distribution and time-domain evolution data; and calculating the instantaneous torque value based on the above signals and data. The technical solution of the present invention can effectively solve the problem of torque signal transmission distortion caused by stress wave reflection at the interface of heterogeneous materials, and improve the accuracy of instantaneous torque measurement.

Description

Technical Field

[0001] The present invention relates to the technical field of aero-engine detection and fault diagnosis, and particularly relates to a method for measuring the instantaneous torque of an aero-engine and a related system. Background Art

[0002] The instantaneous torque of an aero-engine refers to the instantaneous torque transmitted by the shafting under non-steady-state operating conditions, and this parameter reflects the response characteristics and dynamic stability of the engine to sudden commands. The measurement of instantaneous torque is crucial for evaluating the performance of the engine: Firstly, it directly reflects the transient characteristics of the engine and can be used to analyze the dynamic response ability of the engine during acceleration and deceleration; Secondly, by monitoring the change trend of the instantaneous torque, potential fault hazards can be predicted, providing a basis for preventive maintenance of the engine; Thirdly, the instantaneous torque data is of great significance for the optimization and verification of the engine control system, and can help improve the reliability and service life of the engine.

[0003] Currently, the measurement of the instantaneous torque of an aero-engine mainly adopts methods such as strain gauge type, magnetoelectric type, and fiber Bragg grating. Among them, the strain gauge type method indirectly calculates the torque by measuring the surface strain of the shafting, which has the advantages of simple structure and low cost, but has weak anti-interference ability; the magnetoelectric type method utilizes the principle of magnetic flux change and has good dynamic response characteristics, but has insufficient stability in high-temperature environments; the fiber Bragg grating method measures the wavelength change caused by torsion using a fiber optic sensor and has the characteristics of strong anti-electromagnetic interference ability, but the system complexity is high. These methods perform well in measuring the torque of homogeneous material structures, but with the increasingly widespread use of integrated structures of various heterogeneous materials such as titanium alloys, composite materials, and ceramics in modern aero-engines, a common but long-neglected problem has gradually emerged: when the instantaneous torque is transmitted through the heterogeneous material interface, stress wave reflection and refraction phenomena will occur at the interface, resulting in microsecond-level delay and distortion of the torque signal in the time domain. The essence of this stress wave reflection phenomenon at the heterogeneous material interface is the partial reflection of the stress wave caused by the acoustic impedance difference of different materials, and this phenomenon is particularly significant under conditions of high rotational speed and large torque gradient. Since this signal distortion will change dynamically with the engine operating conditions and material temperature and is difficult to eliminate through simple calibration, it seriously affects the accuracy and reliability of the instantaneous torque measurement. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing methods for measuring the instantaneous torque of an aero-engine have signal transmission distortion of the torque caused by stress wave reflection at the heterogeneous material interface.

[0005] The first aspect of the present invention provides a method for measuring the instantaneous torque of an aero-engine, and the method for measuring the instantaneous torque of the aero-engine includes:

[0006] Divide the engine shafting into a first temperature zone, a second temperature zone, and a third temperature zone with increasing temperatures. Apply an acoustic excitation signal with a first frequency to the first temperature zone and obtain a first acoustic response signal. Apply an acoustic excitation signal with a second frequency to the second temperature zone and obtain a second acoustic response signal. Apply an acoustic excitation signal with a third frequency to the third temperature zone and obtain a third acoustic response signal. The first frequency is greater than the second frequency, and the second frequency is greater than the third frequency;

[0007] Determine the engine operating phase point according to the engine speed change rate, and collect the phase point torque response signal at the operating phase point;

[0008] Collect the intershaft crosstalk signals of the low-pressure shaft, high-pressure shaft, and fan shaft of the aeroengine, extract the response time difference and amplitude ratio between adjacent shafts according to the intershaft crosstalk signals, and obtain the intershaft crosstalk characteristic data;

[0009] Collect the aeroengine pressure field information, and extract the pressure field spatial distribution data and pressure field time-domain evolution data according to the pressure field information;

[0010] Calculate the engine instantaneous torque value according to the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the intershaft crosstalk characteristic data, the pressure field spatial distribution data, and the pressure field time-domain evolution data.

[0011] Preferably, the step of dividing the engine shafting into a first temperature zone, a second temperature zone, and a third temperature zone with increasing temperatures, applying an acoustic excitation signal with a first frequency to the first temperature zone and obtaining a first acoustic response signal, applying an acoustic excitation signal with a second frequency to the second temperature zone and obtaining a second acoustic response signal, applying an acoustic excitation signal with a third frequency to the third temperature zone and obtaining a third acoustic response signal includes:

[0012] Divide the single-material region from the intake end of the engine shafting to the front of the compressor into the first temperature zone, divide the double-material region from the compressor to the front of the combustor into the second temperature zone, and divide the multi-material region from the back of the combustor to the turbine into the third temperature zone;

[0013] Calculate the reference frequency coefficient according to the difference between the engine idle speed and the maximum operating speed. Multiply the reference frequency coefficient by the acoustic impedance of the material in the first temperature zone to obtain the acoustic wave propagation frequency attenuation coefficient in the first temperature zone. Set the frequency of the acoustic excitation signal with the first frequency within the range from the first frequency lower limit value determined by the acoustic wave propagation frequency attenuation coefficient to 30 kHz, and apply the acoustic excitation signal with the first frequency to the first temperature zone and obtain the first region response amplitude;

[0014] Calculate the compression frequency coefficient according to the pressure ratio of the engine compressor, multiply the compression frequency coefficient by the acoustic impedance ratio of the material interface in the second temperature zone to obtain the acoustic wave propagation damping coefficient in the second temperature zone, set the frequency of the acoustic excitation signal at the second frequency within the range from the lower limit value of the second frequency determined by the acoustic wave propagation damping coefficient to 20 kHz, apply the acoustic excitation signal at the second frequency to the second temperature zone, and perform interface transfer compensation on the response signal of the second temperature zone according to the response amplitude of the first region to obtain the response amplitude of the second region;

[0015] Calculate the thermoacoustic frequency coefficient according to the gas temperature before the engine turbine, multiply the thermoacoustic frequency coefficient by the acoustic impedance ratio of the multi-layer material in the third temperature zone to obtain the acoustic wave propagation velocity coefficient in the third temperature zone, set the frequency of the acoustic excitation signal at the third frequency within the range from the lower limit value of the third frequency determined by the acoustic wave propagation velocity coefficient to 10 kHz, apply the acoustic excitation signal at the third frequency to the third temperature zone, and obtain the response amplitude of the third region by combining the response amplitude of the first region and the response amplitude of the second region;

[0016] Perform frequency compensation on the response amplitude of the first region to obtain the first acoustic response signal, perform material interface compensation on the response amplitude of the second region to obtain the second acoustic response signal, and perform multi-layer interface compensation on the response amplitude of the third region to obtain the third acoustic response signal.

[0017] Preferably, determining the engine operation phase point according to the engine speed change rate and collecting the phase point torque response signal at the operation phase point includes:

[0018] Obtain the pressure ratios of each stage of the engine compressor and the expansion ratios of each stage of the turbine, calculate the inter-stage power coupling factor according to the change relationship between the pressure ratios of each stage of the compressor, calculate the inter-stage energy conversion factor according to the change relationship between the expansion ratios of each stage of the turbine, and perform an associative operation on the inter-stage power coupling factor and the inter-stage energy conversion factor with the engine speed signal to obtain the compensated speed change rate;

[0019] Collect the inlet guide vane angle of the compressor and the nozzle opening of the turbine, calculate the intake flow correction coefficient according to the corresponding relationship between the guide vane angle and the intake Mach number, calculate the exhaust flow correction coefficient according to the corresponding relationship between the nozzle opening and the back pressure coefficient, and perform polynomial fitting on the compensated speed change rate, the intake flow correction coefficient, and the exhaust flow correction coefficient to obtain the aerodynamic characteristic corrected speed change rate;

[0020] Obtain the temperature gradient and pressure gradient of each stage of the engine, calculate the ratio of the temperature gradient between adjacent stages and the ratio of the pressure gradient between adjacent stages, perform stress wave transfer compensation operation according to the ratio of the temperature gradient and the ratio of the pressure gradient, correct the aerodynamic characteristic corrected speed change rate, and determine the engine operation phase point;

[0021] Calculate the torque sampling compensation coefficient according to the changing trends of the temperature gradient ratios and pressure gradient ratios at all levels, collect the shaft torque signal at the operating phase point, and correct the sampling signal according to the torque sampling compensation coefficient to obtain the torque response signal at the phase point.

[0022] Preferably, collect the inlet guide vane angle of the compressor and the nozzle opening of the turbine, calculate the intake air flow correction coefficient according to the corresponding relationship between the guide vane angle and the intake Mach number, and calculate the exhaust gas flow correction coefficient according to the corresponding relationship between the nozzle opening and the back pressure coefficient, including:

[0023] Obtain the installation angles of the stator blades and the twist angles of the rotor blades at all levels at the inlet of the compressor, perform wavelet transform on the installation angles and twist angles, extract the main frequency characteristics and modulation characteristics of the angle changes, and calculate the cascade passage area coefficient according to the main frequency characteristics and modulation characteristics;

[0024] Collect the static pressure difference and total pressure difference before and after the rotor blades at all levels at the inlet of the compressor, divide the static pressure difference and total pressure difference into a root region, a middle region, and a top region in the blade height direction, use the singular value decomposition method to extract the pressure pulsation characteristics of each region, and calculate the blade load distribution coefficient according to the pressure pulsation characteristics;

[0025] Perform adaptive weighted combination on the cascade passage area coefficient and the blade load distribution coefficient, perform instantaneous frequency analysis on the combination result using the Hilbert-Huang transform to obtain the inlet air passage characteristic coefficient, and calculate the intake Mach number according to the corresponding relationship between the inlet air passage characteristic coefficient and the inlet guide vane angle;

[0026] Obtain the throat area of the stator blades and the outlet area of the rotor blades at all levels of the turbine, collect the clearance pressure and the circumferential air pressure at all levels of the turbine, form a characteristic matrix with the throat area, outlet area, clearance pressure, and circumferential air pressure, and use the principal component analysis method to extract the aerodynamic characteristic vectors;

[0027] Calculate the flow passage convergence factor and the air flow deviation factor according to the aerodynamic characteristic vectors, perform modal separation on the flow passage convergence factor and the air flow deviation factor using the empirical mode decomposition method to obtain the exhaust gas passage characteristic coefficient, and calculate the back pressure coefficient according to the corresponding relationship between the exhaust gas passage characteristic coefficient and the turbine nozzle opening;

[0028] Obtain the intake air flow correction coefficient and the exhaust gas flow correction coefficient respectively according to the non-linear mapping relationship between the intake Mach number and the back pressure coefficient.

[0029] Preferably, collect the inter-axis crosstalk signals of the low-pressure shaft, high-pressure shaft, and fan shaft of the aeroengine, extract the response time difference and amplitude ratio between adjacent shafts according to the inter-axis crosstalk signals to obtain the inter-axis crosstalk characteristic data, including:

[0030] Obtain the torsional vibration signals and radial displacement signals of the low-pressure shaft, high-pressure shaft, and fan shaft. Segment the torsional vibration signals according to the number of compressor stages and turbine stages, and perform time-frequency decomposition on each segment of the signals using continuous wavelet transform to obtain the frequency modulation characteristics of each stage of the shafting;

[0031] Obtain the radial force signals and axial force signals at adjacent bearings, calculate the shafting force distribution coefficient based on the radial force signals and axial force signals, and perform empirical mode decomposition on the shafting force distribution coefficient to obtain the load distribution characteristics of each stage of the shafting;

[0032] Perform tensor decomposition on the frequency modulation characteristics and load distribution characteristics, extract the shafting coupling main mode, and calculate the response time difference between adjacent shafts according to the shafting coupling main mode;

[0033] Perform Hilbert-Huang transform on the shafting coupling main mode, extract the natural frequency characteristics and load frequency characteristics of the shafting as instantaneous frequency characteristics, extract the amplitude characteristics and load amplitude characteristics of the shafting as instantaneous amplitude characteristics, and calculate the amplitude ratio between adjacent shafts according to the instantaneous frequency characteristics and instantaneous amplitude characteristics;

[0034] Perform joint analysis on the response time difference and amplitude ratio using variational mode decomposition, and perform correlation operation on the analysis results and the clearance change law of each stage of the shafting to obtain the inter-axis crosstalk characteristic data.

[0035] Preferably, the method for collecting the aero-engine pressure field information and extracting the pressure field spatial distribution data and pressure field time-domain evolution data according to the pressure field information includes:

[0036] Obtain the pressure signals at the inlet, between stages, and outlet of the engine compressor, perform orthogonal decomposition of the pressure signals in the radial, circumferential, and axial directions, and calculate the pressure field gradient matrix according to the orthogonal decomposition results;

[0037] Use the proper orthogonal decomposition method to perform modal extraction on the pressure field gradient matrix, and reconstruct the extracted dominant modes according to the energy contribution rate to obtain the main mode spatial distribution data;

[0038] Obtain the first pressure pulsation signal in the static blade passage of the engine turbine and the second pressure pulsation signal in the moving blade passage, and perform spatio-temporal evolution analysis on the pressure pulsation signals using the dynamic mode decomposition method to extract the coherent structure of the pressure field;

[0039] Perform Kriging interpolation operation on the main mode spatial distribution data to obtain the pressure field spatial distribution data;

[0040] Perform two-dimensional discrete cosine transform on the coherent structure of the pressure field to obtain the pressure field time-domain evolution data.

[0041] Preferably, the acquiring of a first pressure pulsation signal of a turbine stator passage and a second pressure pulsation signal of a turbine blade passage of an engine, performing a spatiotemporal evolution analysis on the pressure pulsation signals using a dynamic mode decomposition method, and extracting a coherent structure of the pressure field includes:

[0042] obtaining first pressure pulsation signals of the leading edge, trailing edge, pressure surface, and suction surface of the stator blades of the high-pressure stage and the low-pressure stage of the turbine, calculating a stator blade passage pressure distribution matrix based on the first pressure pulsation signals, and performing singular spectrum decomposition on the stator blade passage pressure distribution matrix to obtain a stator blade passage pressure characteristic mode;

[0043] obtaining second pressure pulsation signals of the blade shroud, blade basin, and blade back of the turbine high-pressure stage and low-pressure stage, calculating a pressure distribution matrix of the blade passage based on the second pressure pulsation signals, and performing singular spectrum decomposition on the blade passage pressure distribution matrix to obtain a pressure characteristic mode of the blade passage;

[0044] performing a nonlinear superposition operation on the stationary blade passage pressure characteristic mode and the moving blade passage pressure characteristic mode according to a variation law of the turbine interstage clearance to obtain a cascade passage pressure coupling coefficient;

[0045] The dynamic mode decomposition method is used to perform time series analysis on the pressure coupling coefficient of the cascade channel, and aerodynamic compensation is performed according to the changing relationship between the turbine inlet airflow angle and the outlet airflow angle to obtain the pressure field coherent structure.

[0046] Preferably, the instantaneous torque value of the engine is calculated based on the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the inter-axle crosstalk characteristic data, the pressure field spatial distribution data, and the pressure field time domain evolution data, including:

[0047] Dividing the first acoustic response signal, the second acoustic response signal, and the third acoustic response signal into a single material segment, a double-layer material segment, and a multi-layer material segment, performing an acoustic wave reflection analysis on the single material segment, performing an interface transmission analysis on the double-layer material segment, and performing a diffraction characteristic analysis on the multi-layer material segment, performing acoustic compensation in combination with the acoustic impedance coefficients of the materials at each level of the shafting, and obtaining an acoustic characteristic weight coefficient;

[0048] The covariance matrix decomposition is performed based on the phase point torque response signal and the inter-axis crosstalk characteristic data, and energy compensation is performed in combination with the engine compressor efficiency characteristics and turbine efficiency characteristics to obtain the power characteristic weight coefficient;

[0049] Based on the pressure field spatial distribution data and the pressure field time domain evolution data, subspace mapping analysis is performed, and the flow field compensation is performed by combining the aerodynamic load coefficients of each stage of the turbine and the blade parameters of each stage of the compressor to obtain the aerodynamic characteristic weight coefficient;

[0050] Construct a tensor set from the acoustic feature weight coefficients, dynamic feature weight coefficients, and aerodynamic feature weight coefficients, perform feature fusion using a deep sparse autoencoder, and perform adaptive correction in combination with the engine speed and power characteristics to obtain a fused weight matrix;

[0051] Perform a weighted fusion operation on the first acoustic response signal, second acoustic response signal, third acoustic response signal, phase point torque response signal, inter-axis crosstalk feature data, pressure field spatial distribution data, and pressure field time-domain evolution data according to the fused weight matrix to obtain the engine instantaneous torque value.

[0052] The second aspect of the present invention provides an instantaneous torque test device for an aeroengine, and the instantaneous torque test device for the aeroengine includes:

[0053] An acoustic response module, configured to divide the engine shafting into a first temperature zone, a second temperature zone, and a third temperature zone with increasing temperature, apply an acoustic excitation signal with a first frequency to the first temperature zone and obtain a first acoustic response signal, apply an acoustic excitation signal with a second frequency to the second temperature zone and obtain a second acoustic response signal, apply an acoustic excitation signal with a third frequency to the third temperature zone and obtain a third acoustic response signal, where the first frequency is greater than the second frequency, and the second frequency is greater than the third frequency;

[0054] A phase response module, configured to determine the engine operating phase point according to the engine speed change rate and collect the phase point torque response signal at the operating phase point;

[0055] A crosstalk analysis module, configured to collect the inter-axis crosstalk signals of the low-pressure shaft, high-pressure shaft, and fan shaft of the aeroengine, extract the response time difference and amplitude ratio between adjacent shafts according to the inter-axis crosstalk signals, and obtain the inter-axis crosstalk feature data;

[0056] A pressure field analysis module, configured to collect the aeroengine pressure field information and extract the pressure field spatial distribution data and pressure field time-domain evolution data according to the pressure field information;

[0057] A torque calculation module, configured to calculate the engine instantaneous torque value according to the first acoustic response signal, second acoustic response signal, third acoustic response signal, phase point torque response signal, inter-axis crosstalk feature data, pressure field spatial distribution data, and pressure field time-domain evolution data.

[0058] The third aspect of the present invention provides an instantaneous torque test system for an aeroengine, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected by a line; the at least one processor calls the instructions in the memory to enable the instantaneous torque test system of the aeroengine to execute the steps of the above-mentioned instantaneous torque test method for the aeroengine.

[0059] The fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored. When it runs on a computer, it enables the computer to execute the steps of the above-mentioned instantaneous torque test method for the aeroengine.

[0060] In order to solve the problem of torque signal distortion caused by stress wave reflection at the interface of heterogeneous materials, the present invention proposes a new measurement method. This method cracks the problem of stress wave reflection through acoustic partition excitation: since the interface of heterogeneous materials will cause stress wave reflection and refraction, directly measuring the torque signal will produce distortion. Therefore, the present invention divides the shafting into three regions according to the temperature gradient, and applies acoustic excitations with different frequencies in different regions. Through reasonable frequency selection, relatively independent acoustic wave propagation characteristics can be formed within each region, so that the influence of the heterogeneous material interface on stress wave transmission is limited to a specific region. In this way, the acoustic response signal obtained in each region can accurately reflect the torque transmission characteristics within that region.

[0061] Since the working state of the engine will affect the transmission of the torque signal, the present invention determines the key operating phase points by analyzing the rotational speed change rate, and collects the torque response signals at these moments. This method avoids the problem that the traditional fixed sampling method may miss important transient characteristics. The torque signal collected at the phase points can truly reflect the torque change law of the engine under dynamic working conditions.

[0062] The multi-shaft structure of the engine will cause interference between shafts, affecting the accurate measurement of torque. The present invention collects the crosstalk signals of the low-pressure shaft, high-pressure shaft and fan shaft simultaneously, extracts the time difference and amplitude ratio between adjacent shafts, and establishes the crosstalk characteristic data between shafts. These data reveal the interaction law between different shaft systems, providing a basis for eliminating the influence of crosstalk between shafts.

[0063] In the processing of pressure field data, the present invention obtains the influence law of flow field change on torque transmission by extracting the spatial distribution and time-domain evolution characteristics of the pressure field. The pressure field characteristics are directly related to torque transmission and can reflect the stress transmission state at the interface of heterogeneous materials.

[0064] Finally, the present invention performs fusion processing on the information in the above-mentioned multiple dimensions to obtain the final instantaneous torque value. This fusion processing mechanism ensures that the true torque signal can be accurately captured under any working conditions, effectively overcoming the signal distortion problem caused by the interface of heterogeneous materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on the structures shown in these drawings.

[0066] Figure 1 It is a schematic diagram of an embodiment of the instantaneous torque test method for an aero-engine in an embodiment of the present invention;

[0067] Figure 2 It is a schematic diagram of an embodiment of the instantaneous torque test device for an aero-engine in an embodiment of the present invention;

[0068] Figure 3 It is a schematic diagram of an embodiment of the instantaneous torque test system for an aero-engine in an embodiment of the present invention.

[0069] The realization of the object, functional features and advantages of the present invention will be further described in conjunction with the embodiments and with reference to the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0071] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, the directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0072] In addition, the descriptions involving "first", "second", etc. in the present invention are for descriptive purposes only, and should not be construed as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, "and / or" throughout the text includes three scenarios. Taking A and / or B as an example, it includes the technical solution of A, the technical solution of B, and the technical solution where both A and B are satisfied simultaneously. In addition, the technical solutions between various embodiments can be combined with each other, which must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0073] An embodiment of the present application provides a method for testing the instantaneous torque of an aeroengine. Figure 1 FIG. is a flowchart of a method for testing the instantaneous torque of an aeroengine provided by an embodiment of the present application. In this embodiment, the method includes:

[0074] Please refer to Figure 1 , divide the engine shafting into a first temperature zone, a second temperature zone, and a third temperature zone with increasing temperatures, apply an acoustic excitation signal with a first frequency to the first temperature zone and obtain a first acoustic response signal, apply an acoustic excitation signal with a second frequency to the second temperature zone and obtain a second acoustic response signal, apply an acoustic excitation signal with a third frequency to the third temperature zone and obtain a third acoustic response signal, where the first frequency is greater than the second frequency, and the second frequency is greater than the third frequency;

[0075] In an embodiment of the present invention, the step of dividing the engine shafting into a first temperature zone, a second temperature zone, and a third temperature zone with increasing temperatures, applying an acoustic excitation signal with a first frequency to the first temperature zone and obtaining a first acoustic response signal, applying an acoustic excitation signal with a second frequency to the second temperature zone and obtaining a second acoustic response signal, applying an acoustic excitation signal with a third frequency to the third temperature zone and obtaining a third acoustic response signal includes:

[0076] Divide the single-material region from the intake end of the engine shafting to the front of the compressor into the first temperature zone, divide the double-material region from the compressor to the front of the combustor into the second temperature zone, and divide the multi-material region from the back of the combustor to the turbine into the third temperature zone;

[0077] Calculate the reference frequency coefficient based on the difference between the engine idle speed and the maximum operating speed, multiply the reference frequency coefficient by the acoustic impedance of the material in the first temperature zone to obtain the acoustic wave propagation frequency attenuation coefficient in the first temperature zone, set the frequency of the acoustic excitation signal at the first frequency within the range from the lower limit value of the first frequency determined by the acoustic wave propagation frequency attenuation coefficient to 30 kHz, apply the acoustic excitation signal at the first frequency to the first temperature zone and obtain the response amplitude of the first region;

[0078] Calculate the compression frequency coefficient according to the pressure ratio of the engine compressor, multiply the compression frequency coefficient by the acoustic impedance ratio of the material interface in the second temperature zone to obtain the acoustic wave propagation damping coefficient in the second temperature zone, set the frequency of the acoustic excitation signal at the second frequency within the range from the lower limit value of the second frequency determined by the acoustic wave propagation damping coefficient to 20 kHz, apply the acoustic excitation signal at the second frequency to the second temperature zone, and perform interface transfer compensation on the response signal of the second temperature zone according to the response amplitude of the first region to obtain the response amplitude of the second region;

[0079] Calculate the thermoacoustic frequency coefficient according to the gas temperature before the engine turbine, multiply the thermoacoustic frequency coefficient by the acoustic impedance ratio of the multi-layer material in the third temperature zone to obtain the acoustic wave propagation velocity coefficient in the third temperature zone, set the frequency of the acoustic excitation signal at the third frequency within the range from the lower limit value of the third frequency determined by the acoustic wave propagation velocity coefficient to 10 kHz, apply the acoustic excitation signal at the third frequency to the third temperature zone, and obtain the response amplitude of the third region by combining the response amplitude of the first region and the response amplitude of the second region;

[0080] Perform frequency compensation on the response amplitude of the first region to obtain the first acoustic response signal, perform material interface compensation on the response amplitude of the second region to obtain the second acoustic response signal, and perform multi-layer interface compensation on the response amplitude of the third region to obtain the third acoustic response signal.

[0081] The following specifically describes the steps involved in the above embodiments:

[0082] First step, divide the engine shafting into zones. This step scans each part of the shafting using a thermocouple array and an acoustic detection device, and determines the precise zone boundaries in combination with the engine structure drawings. The area from the intake end to the front of the compressor is mainly composed of a single titanium alloy or nickel-based superalloy, with a relatively low temperature (30°C - 200°C) and relatively stable acoustic wave propagation characteristics, so it is divided into the first temperature zone; the area from the compressor to the front of the combustion chamber is a double-layer structure composed of an inner high-temperature alloy and an outer heat-insulating material, with a medium temperature (200°C - 600°C), and acoustic waves will produce a certain degree of reflection and refraction at the material interface, so it is divided into the second temperature zone; the area from the back of the combustion chamber to the turbine is composed of multiple layers of materials such as a high-temperature alloy matrix, a thermal barrier coating, a heat-insulating layer, and a cooling structure, with the highest temperature (600°C - 1500°C) and the highest complexity of acoustic wave propagation, so it is divided into the third temperature zone. This division method based on material composition and temperature gradient can specifically solve the problem of different acoustic wave propagation characteristics caused by material composition and temperature differences in different zones, making the subsequent acoustic analysis more accurate.

[0083] Second step, perform acoustic excitation and response acquisition on the first temperature zone. This step first reads the idle speed (e.g., 4000 rpm) and the maximum operating speed (e.g., 12000 rpm) from the engine control unit (ECU), calculates the difference as 8000 rpm, and multiplies this difference by the acoustic conversion factor (0.002 Hz / rpm) to obtain the reference frequency coefficient of 16 Hz. Then use an acoustic impedance analyzer to measure the acoustic impedance value (27.3× kg / m²s) of the material (such as Ti-6Al-4V titanium alloy) in the first temperature zone, and multiply it by the reference frequency coefficient to obtain the acoustic wave propagation frequency attenuation coefficient of 0.437× . Determine the first frequency lower limit value as 26.2 kHz according to this coefficient, and select 28 kHz as the excitation frequency in the range from this lower limit to 30 kHz. Use a piezoelectric transducer array with precise frequency control to apply an acoustic excitation signal to the first temperature zone, collect the response signal through high-sensitivity piezoelectric sensors arranged on the surface of the shafting, and obtain the response amplitude of the first zone through a high-speed data acquisition system and spectral analysis processing. The high-frequency range (26.2 kHz - 30 kHz) is selected because the first temperature zone has a low temperature and a single material, and high-frequency acoustic waves can provide higher measurement resolution and improve the torque measurement accuracy.

[0084] Third step, perform acoustic excitation and response acquisition on the second temperature zone. This step first obtains the pressure ratio of the compressor (e.g., 8:1) from the engine monitoring system, and substitutes it into the acoustic compression coefficient calculation formula =0.15×(PR - 1), where, Let \(\omega_c\) denote the compression frequency coefficient, \(PR\) denote the compressor pressure ratio, and 0.15 is an empirical constant used to convert the change in pressure ratio into an acoustic frequency impact factor. The compression frequency coefficient \(\omega_c = 1.05\) is obtained. Then, using an interfacial acoustic analyzer, the acoustic impedance ratio (1.62) of the inner and outer layer materials (such as Inconel 718 and thermal insulation ceramics) in the second temperature zone is measured, and multiplying it by the compression frequency coefficient gives the acoustic wave propagation damping coefficient \(1.701\). Based on this coefficient, the lower limit of the second frequency is determined to be \(15.3\) kHz, and \(17\) kHz is selected as the excitation frequency within the range from this lower limit to \(20\) kHz. An intermediate-frequency piezoelectric transducer is used to apply an acoustic excitation signal to the second temperature zone, the response signal is collected through a distributed acoustic sensing network, and using a digital signal processor and combining with the response amplitude of the first region obtained in the first step, the interface transfer function algorithm is applied to eliminate the signal distortion caused by interface reflection, and the response amplitude of the second region is obtained. The intermediate-frequency range (\(15.3\) kHz - \(20\) kHz) is selected because there is a material interface in the second temperature zone. Intermediate-frequency acoustic waves can not only maintain sufficient penetration ability but also provide sufficient resolution, balancing the measurement depth and accuracy requirements.

[0085] In the fourth step, acoustic excitation and response acquisition are performed on the third temperature zone. This step first obtains the gas temperature in front of the turbine (e.g., \(1100^{\circ}C\)) through an array of temperature sensors and substitutes it into the thermoacoustic conversion equation \(\omega_t=0.004\times(T - 800)\), where \(\omega_t\) denotes the thermoacoustic frequency coefficient, \(T\) denotes the gas temperature in front of the turbine (\(^{\circ}C\)), 0.004 is the thermoacoustic conversion coefficient, and 800 is the reference temperature (\(^{\circ}C\)) used to calculate the influence of high temperature on the acoustic wave propagation characteristics. The thermoacoustic frequency coefficient \(\omega_t = 1.2\) is calculated. Then, using a multi-layer acoustic scanning system, the comprehensive acoustic impedance ratio (2.83) of each layer of materials (such as superalloy, ceramic thermal barrier coating, thermal insulation layer) in the third temperature zone is measured, and multiplying it by the thermoacoustic frequency coefficient gives the acoustic wave propagation velocity coefficient \(3.396\). Based on this coefficient, the lower limit of the third frequency is determined to be \(6.8\) kHz, and \(8\) kHz is selected as the excitation frequency within the range from this lower limit to \(10\) kHz. A low-frequency piezoelectric transducer resistant to high temperature is used to apply an acoustic excitation signal to the third temperature zone, the response signal is collected through a high-temperature-resistant acoustic sensor network, and combining with the response amplitudes obtained in the previous two steps, a multi-region signal correlation processor is used for comprehensive analysis to obtain the response amplitude of the third region. The low-frequency range (\(6.8\) kHz - \(10\) kHz) is selected because the third temperature zone has a high temperature and complex materials. Low-frequency acoustic waves have strong penetration ability and can penetrate through multiple material interfaces to obtain complete acoustic information, ensuring reliable measurement results can still be obtained in the high-temperature complex material region.

[0086] Step 5: Compensate the response amplitudes of each region. This step uses a dedicated acoustic signal processing system to apply a frequency response function to the response amplitude of the first region for frequency compensation to eliminate amplitude distortion caused by uneven frequency response in the high-frequency band; apply an acoustic impedance matching algorithm to the response amplitude of the second region for material interface compensation to correct signal attenuation and phase shift caused by reflection and refraction at the bilayer material interface; apply a multi-layer acoustic propagation model to the response amplitude of the third region for multi-layer interface compensation to correct response distortion caused by complex interference of multi-layer materials. During the compensation process, an iterative optimization algorithm is used to ensure the optimal selection of compensation parameters, and finally, the first acoustic response signal, the second acoustic response signal, and the third acoustic response signal reflecting the true torque transmission characteristics of each temperature zone are obtained respectively. This differentiated compensation processing method fully considers the differences in material characteristics and acoustic propagation characteristics in different temperature regions, avoids the problem of cumulative measurement errors caused by traditional single processing methods, and improves the overall measurement accuracy.

[0087] Please continue to refer to Figure 1 , determine the engine operating phase point according to the engine speed change rate, and collect the phase point torque response signal at the operating phase point;

[0088] In an embodiment of the present invention, the determining the engine operating phase point according to the engine speed change rate and collecting the phase point torque response signal at the operating phase point includes:

[0089] Obtain the pressure ratios of each stage of the engine compressor and the expansion ratios of each stage of the turbine, calculate the inter-stage power coupling factor according to the change relationship between the pressure ratios of each stage of the compressor, calculate the inter-stage energy conversion factor according to the change relationship between the expansion ratios of each stage of the turbine, and perform an association operation on the inter-stage power coupling factor and the inter-stage energy conversion factor with the engine speed signal to obtain a compensated speed change rate;

[0090] Collect the inlet guide vane angle of the compressor and the nozzle opening of the turbine, calculate the intake flow correction coefficient according to the corresponding relationship between the guide vane angle and the intake Mach number, calculate the exhaust flow correction coefficient according to the corresponding relationship between the nozzle opening and the back pressure coefficient, and perform polynomial fitting on the compensated speed change rate, the intake flow correction coefficient, and the exhaust flow correction coefficient to obtain an aerodynamic characteristic corrected speed change rate;

[0091] Obtain the temperature gradient and pressure gradient of each stage of the engine, calculate the temperature gradient ratio and pressure gradient ratio between adjacent stages, perform stress wave transmission compensation operation according to the temperature gradient ratio and pressure gradient ratio, correct the aerodynamic characteristic corrected speed change rate, and determine the engine operating phase point;

[0092] Calculate the torque sampling compensation coefficient according to the changing trends of the temperature gradient ratios and pressure gradient ratios at all levels, collect the shaft torque signal at the operating phase point, and correct the sampling signal according to the torque sampling compensation coefficient to obtain the torque response signal at the phase point.

[0093] The following specifically describes the steps involved in the above embodiments:

[0094] The first step is to obtain the parameters of each stage of the engine and calculate the coupling factors. In this step, the pressure ratio data of each stage is collected through a pressure sensor array installed between the stages of the compressor (for example, the first stage is 1.8:1, the second stage is 1.6:1, and the third stage is 1.4:1), and the expansion ratio data of each stage is collected through the temperature and pressure sensors of each stage of the turbine (for example, the high-pressure stage is 2.5:1 and the low-pressure stage is 2.0:1). When calculating the inter-stage power coupling factor, first calculate the difference in pressure ratio between adjacent compressor stages, then divide it by the pressure ratio of the previous stage, and then sum over all stages and multiply by the power conversion constant (with a value of 0.8). When calculating the inter-stage energy conversion factor, divide the product of the expansion ratios of adjacent turbine stages by their sum, sum over all stages and multiply by the energy conversion constant (with a value of 1.2). Finally, combine the change rate of the engine speed with these two factors. Specifically, multiply the change rate of the speed by (1 plus the power coupling factor minus the energy conversion factor) to obtain the compensated change rate of the speed. This method considering the relationship between the compressor and turbine stages can accurately reflect the power transfer characteristics in the multi-stage compression and expansion processes and improve the accuracy of the change rate of the speed.

[0095] The second step is to collect the pneumatic parameters and perform corrections. In this step, the inlet guide vane angle of the compressor (for example, 23°) and the nozzle opening of the turbine (for example, 85%) are collected through position sensors. When calculating the intake flow correction coefficient, based on the deviation value of the guide vane angle from the optimal guide vane angle (usually 20°), use a linear relationship to obtain the correction value. Specifically, it is 0.95 minus the product of the deviation value and the coefficient 0.005. When calculating the exhaust flow correction coefficient, based on the difference between the nozzle opening and the nominal nozzle opening (usually 80%), use a linear relationship to obtain the correction value. Specifically, it is 0.9 plus the product of the difference and the coefficient 0.002. Then, use a polynomial fitting program to perform a weighted combination of the compensated change rate of the speed, the intake flow correction coefficient, and the exhaust flow correction coefficient. The weight coefficients are determined through experiments to obtain the corrected change rate of the speed of the pneumatic characteristics. This multi-parameter fitting method combines the influence of the intake and exhaust characteristics on the speed change and more comprehensively reflects the dynamic pneumatic characteristics of the engine.

[0096] Step 3: Obtain gradient information and determine the operating phase point. In this step, temperature gradients (such as 120 °C / stage) and pressure gradients (such as 200 kPa / stage) of each stage of the engine are obtained through a temperature sensor and a pressure sensor array. Calculate the temperature gradient ratio between adjacent stages (i.e., the temperature gradient of the current stage divided by the temperature gradient of the next stage) and the pressure gradient ratio (i.e., the pressure gradient of the current stage divided by the pressure gradient of the next stage). When performing stress wave transmission compensation, multiply the aerodynamic characteristic corrected rotational speed change rate by a compensation factor, which is obtained by summing the temperature gradient ratio and the pressure gradient ratio multiplied by their respective weight coefficients. Use numerical analysis software to find the inflection point or extreme point of the final corrected rotational speed change rate to determine the engine operating phase point. This method considering temperature and pressure gradients can capture the variation characteristics of stress waves during transmission at different material interfaces and more accurately determine the optimal moment for torque sampling.

[0097] Step 4: Calculate the compensation coefficient and collect torque signals. When calculating the torque sampling compensation coefficient in this step, consider the change rates of the temperature gradient ratio and the pressure gradient ratio (i.e., their degrees of change over time). Sum the change rates of the temperature gradient ratios of each stage multiplied by the corresponding influence coefficients, add the sum of the change rates of the pressure gradient ratios of each stage multiplied by the corresponding influence coefficients, and finally add the reference value 1 to obtain the compensation coefficient. At the determined operating phase point, use a high-precision torque sensor to collect the original torque signal of the shafting, and multiply this signal by the calculated torque sampling compensation coefficient to obtain the torque response signal at the phase point. This compensation method based on the gradient change trend can effectively eliminate the distortion of the torque signal caused by the reflection of stress waves at the heterogeneous material interface and improve the accuracy of instantaneous torque measurement, especially in non-steady-state conditions such as rapid acceleration and deceleration of the engine.

[0098] In an embodiment of the present invention, the collecting the inlet guide vane angle of the compressor and the nozzle opening of the turbine, calculating the intake air flow correction coefficient according to the corresponding relationship between the guide vane angle and the intake Mach number, and calculating the exhaust gas flow correction coefficient according to the corresponding relationship between the nozzle opening and the back pressure coefficient includes:

[0099] Obtain the installation angles of the stator blades and the twist angles of the rotor blades of each stage at the inlet of the compressor, perform wavelet transform on the installation angles and twist angles, extract the main frequency characteristics and modulation characteristics of the angle changes, and calculate the cascade passage area coefficient according to the main frequency characteristics and modulation characteristics;

[0100] Collect the static pressure difference and total pressure difference before and after the rotor blades of each stage at the inlet of the compressor, divide the static pressure difference and total pressure difference into a root region, a middle region, and a top region in the blade height direction, use the singular value decomposition method to extract the pressure pulsation characteristics of each region, and calculate the blade load distribution coefficient according to the pressure pulsation characteristics;

[0101] The area coefficient of the cascade passage and the blade loading distribution coefficient are adaptively weighted and combined, and the Hilbert-Huang transform is used to perform instantaneous frequency analysis on the combined result to obtain the characteristic coefficient of the inlet flow passage. The inlet Mach number is calculated according to the corresponding relationship between the characteristic coefficient of the inlet flow passage and the inlet guide vane angle;

[0102] Obtain the throat area of the stator blades and the outlet area of the rotor blades at each stage of the turbine, collect the clearance pressure and the circumferential air pressure at each stage of the turbine, form a characteristic matrix with the throat area, the outlet area, the clearance pressure and the circumferential air pressure, and use the principal component analysis method to extract the aerodynamic characteristic vector;

[0103] Calculate the flow passage convergence factor and the air flow deviation factor according to the aerodynamic characteristic vector, use the empirical mode decomposition method to perform mode separation on the flow passage convergence factor and the air flow deviation factor to obtain the characteristic coefficient of the exhaust flow passage, and calculate the back pressure coefficient according to the corresponding relationship between the characteristic coefficient of the exhaust flow passage and the turbine nozzle opening;

[0104] According to the non-linear mapping relationship between the inlet Mach number and the back pressure coefficient, obtain the inlet flow rate correction coefficient and the exhaust flow rate correction coefficient respectively.

[0105] The following specifically describes the steps involved in the above embodiments:

[0106] First step, obtain the installation angle of the compressor stator blades and the twist angle of the rotor blades and calculate the area coefficient of the cascade passage. This step uses an optical angle measuring instrument to obtain the installation angles of the stator blades at each stage of the compressor inlet (for example, 40° for the first stage and 38° for the second stage) and the twist angles of the rotor blades (for example, 12° for the first stage and 15° for the second stage). Use a digital signal processor to perform discrete wavelet transform on these angle data, use Daubechies wavelet as the basis function, and set the decomposition level to 5 levels. By analyzing the wavelet coefficients, extract the main frequency characteristics of the angle change (the frequency components representing periodic changes, such as 7.5 Hz related to the rotational speed and 230 Hz of the blade passing frequency) and the modulation characteristics (the modulation frequency representing the amplitude change, such as 2.3 Hz related to the intake pulsation). The main frequency characteristics reflect the change in the flow passage area caused by the periodic swing of the blades, and the modulation characteristics reflect the contraction or expansion of the flow passage caused by non-periodic disturbances. According to these characteristics, use a weighted calculation method to obtain the area coefficient of the cascade passage, which represents the ratio of the effective area of the flow passage between the blades to the designed area. This wavelet analysis-based method can accurately capture the dynamic change characteristics of the blade angles and reflect the real-time flow capacity of the cascade passage.

[0107] Second step, collect and analyze the pressure difference information before and after the compressor rotor blades to calculate the load distribution coefficient. In this step, the static pressure difference (such as 4.2 kPa for the first stage and 3.8 kPa for the second stage) and the total pressure difference (such as 15.6 kPa for the first stage and 14.2 kPa for the second stage) data are collected by a micro pressure sensor array installed before and after the rotor blades of each stage of the compressor. The collected pressure difference data are divided into three regions in the blade height direction: the root region accounting for 0 - 30% of the blade height, the middle region accounting for 30 - 70% of the blade height, and the tip region accounting for 70 - 100% of the blade height. A time series matrix is constructed for the pressure data in each region, and the matrix is decomposed using the singular value decomposition method. The first few singular values that account for more than 90% of the total energy and their corresponding singular vectors are retained to extract the pressure pulsation characteristics of each region. The magnitude of the singular value reflects the intensity of the pressure pulsation, and the singular vector reflects the spatial distribution pattern of the pulsation. The blade load distribution coefficient is calculated based on these characteristics, and this coefficient represents the relative distribution of the loads in each region of the blade. This multi-region analysis method can accurately characterize the non-uniform radial distribution of the blade load and effectively identify complex aerodynamic phenomena such as local flow separation and secondary flow.

[0108] Third step, perform combined analysis and calculate the inlet Mach number. In this step, the cascade passage area coefficient and the blade load distribution coefficient are input into an adaptive weighted algorithm, and the weights of the two coefficients are automatically adjusted according to the current engine operating conditions to obtain a combined result. The adaptive weights are calculated in real time by the engine control system based on parameters such as the rotational speed and power to ensure a reasonable degree of emphasis on the two coefficients under different operating conditions. The Hilbert-Huang transform is applied to the combined result to decompose the signal into multiple intrinsic mode functions and extract the instantaneous frequency characteristics. The Hilbert-Huang transform is a method suitable for the analysis of non-linear and non-stationary signals and can effectively handle the rapid change characteristics of the airflow parameters of aeroengines. The inlet flow passage characteristic coefficient is obtained based on the instantaneous frequency characteristics, and then the inlet Mach number is calculated according to the calibration relationship curve (pre-obtained through wind tunnel tests) between this coefficient and the inlet guide vane angle. This adaptive combination and advanced signal processing method significantly improve the accuracy of the inlet Mach number calculation and meet the operating requirements of the engine under all conditions.

[0109] Step 4: Obtain the turbine aerodynamic parameters and construct the feature matrix. In this step, the throat areas of the stator blades of each stage of the turbine (e.g., 400 mm² for the high-pressure stage and 600 mm² for the low-pressure stage) and the outlet areas of the rotor blades (e.g., 450 mm² for the high-pressure stage and 700 mm² for the low-pressure stage) are obtained through engine design data and real-time measurements. At the same time, the clearance pressures of each stage of the turbine (e.g., 800 kPa for the high-pressure stage and 400 kPa for the low-pressure stage) and the air pressure data at multiple points on the wheel circumference (one measurement point every 45° along the circumferential direction) are collected through a pressure sensor array. These data are organized into a feature matrix, where the rows of the matrix represent different measurement points and the columns represent different parameters. Apply the principal component analysis method to this feature matrix, calculate the covariance matrix, and solve its eigenvalues and eigenvectors. Select the first few principal components with a contribution rate exceeding 95% as the aerodynamic feature vectors. Principal component analysis can effectively reduce the data dimension, extract key information, and eliminate redundancy and noise interference between parameters. This multi-parameter comprehensive analysis method can comprehensively capture the geometric characteristics and aerodynamic performance of the turbine flow passage.

[0110] Step 5: Analyze the aerodynamic feature vectors and calculate the backpressure coefficient. In this step, the flow passage convergence factor (representing the change rate of the flow passage cross-sectional area along the flow direction) and the air flow deviation factor (representing the deviation degree between the actual air flow direction and the designed direction) are calculated based on the aerodynamic feature vectors. Apply the empirical mode decomposition method to these two factors to decompose the signal into multiple intrinsic mode functions and a residual trend term, and extract the modes directly related to the turbine aerodynamic performance. Empirical mode decomposition is an adaptive signal processing method suitable for processing non-linear and non-stationary aerodynamic signals. The exhaust passage characteristic coefficient is obtained through the reconstruction of the key modes, which comprehensively reflects the flow capacity of the exhaust system. Calculate the backpressure coefficient according to the calibration relationship curve between this coefficient and the turbine nozzle opening (obtained in advance through a high-temperature aerodynamic test bench). This multi-step analysis method can accurately characterize the complex aerodynamic characteristics of the exhaust system and adapt to different engine exhaust configurations.

[0111] Step 6: Calculate the flow correction coefficients based on the intake and exhaust parameters. In this step, a non-linear mapping relationship between the intake Mach number and the backpressure coefficient is established using the numerical simulation results. Through table lookup or interpolation methods, according to the currently calculated intake Mach number and backpressure coefficient, the intake flow correction coefficient and the exhaust flow correction coefficient are obtained respectively. The non-linear mapping relationship takes into account various factors such as gas compressibility, flow passage geometry, and gas state equation, and can accurately reflect the complex relationship between the intake and exhaust flows, Mach numbers, and backpressures. This correction method based on a physical model can significantly improve the accuracy of flow estimation and provide reliable aerodynamic basic data for instantaneous torque measurement.

[0112] Please continue to refer to Figure 1, collect the intershaft crosstalk signals of the low-pressure shaft, high-pressure shaft and fan shaft of the aeroengine, extract the response time difference and amplitude ratio between adjacent shafts according to the intershaft crosstalk signals, and obtain the intershaft crosstalk characteristic data;

[0113] In an embodiment of the present invention, the collecting the intershaft crosstalk signals of the low-pressure shaft, high-pressure shaft and fan shaft of the aeroengine, extracting the response time difference and amplitude ratio between adjacent shafts according to the intershaft crosstalk signals, and obtaining the intershaft crosstalk characteristic data includes:

[0114] Obtain the torsional vibration signals and radial displacement signals of the low-pressure shaft, high-pressure shaft and fan shaft, segment the torsional vibration signals according to the number of compressor stages and turbine stages, and perform time-frequency decomposition on each segment of the signals by using continuous wavelet transform to obtain the frequency modulation characteristics of each stage of the shafting;

[0115] Obtain the radial force signals and axial force signals at adjacent bearings, calculate the shafting force distribution coefficient according to the radial force signals and axial force signals, and perform empirical mode decomposition on the shafting force distribution coefficient to obtain the load distribution characteristics of each stage of the shafting;

[0116] Perform tensor decomposition on the frequency modulation characteristics and load distribution characteristics, extract the coupled main modes of the shafting, and calculate the response time difference between adjacent shafts according to the coupled main modes of the shafting;

[0117] Perform Hilbert-Huang transform on the coupled main modes of the shafting, extract the natural frequency characteristics and load frequency characteristics of the shafting as instantaneous frequency characteristics, extract the amplitude characteristics and load amplitude characteristics of the shafting as instantaneous amplitude characteristics, and calculate the amplitude ratio between adjacent shafts according to the instantaneous frequency characteristics and instantaneous amplitude characteristics;

[0118] Perform joint analysis on the response time difference and amplitude ratio by using variational mode decomposition, and perform correlation operation on the analysis result and the clearance change law of each stage of the shafting to obtain the intershaft crosstalk characteristic data.

[0119] The following specifically describes the steps involved in the above embodiments:

[0120] The first step is to acquire and process the shafting vibration signals. This step collects torsional vibration signals and radial displacement signals through optical torsional vibration sensors and eddy current displacement sensors installed on the low-pressure shaft, high-pressure shaft, and fan shaft of the aeroengine. The torsional vibration signals reflect the angular displacement changes that occur during the rotation of the shafting, while the radial displacement signals reflect the radial swing of the shaft. The collected signals are segmented according to the number of stages of the engine compressor and turbine. For example, if a certain type of engine includes 9 stages of compressors and 4 stages of turbines, the signals are divided into 13 segments. Continuous wavelet transform is used to perform time-frequency decomposition on each segment of the signals. Specifically, the Mexican hat wavelet is selected as the mother wavelet, the scale parameter is set to 1 - 64, and the translation step size is 1. Wavelet coefficients are calculated for each segment of the signals to generate a time-frequency energy distribution diagram. The several frequency components with the strongest signal energy and their variation patterns over time are extracted from the time-frequency diagram to obtain frequency modulation characteristics. For example, for a certain segment of the signal of the high-pressure shaft, it may be found that there is a fundamental frequency of 120 Hz, and there is obvious amplitude modulation related to the rotational speed. This analysis method based on continuous wavelet transform can accurately capture the frequency variation characteristics of the shafting during the transfer process between different stages, and reflect the influence of the interface of heterogeneous materials on torque transfer.

[0121] The second step is to acquire and process the bearing force signals. This step collects radial force signals (x direction and y direction) and axial force signals (z direction) through a strain gauge force sensor array installed at each bearing housing of the engine. These force signals reflect the support reaction forces received by the shafting during operation. According to the collected force signals, the magnitude and direction of the resultant force at each bearing are calculated, and considering the relative positions between the bearings, the force distribution coefficient of the shafting is calculated. This coefficient represents the uniformity of the force distribution on the shafting. The calculation method is to divide the force value at each bearing by the average value of all bearing force values to obtain a series of dimensionless coefficients. The empirical mode decomposition method is applied to these shafting force distribution coefficients to decompose the signals into multiple intrinsic mode functions and a residual trend. Empirical mode decomposition is an adaptive signal processing method that can decompose complex signals into components with clear physical meanings. The main modes related to the dynamic characteristics of the shafting are identified from the intrinsic mode functions to obtain the load distribution characteristics of each stage of the shafting. For example, for the high-pressure shaft of a certain type of engine, it may be found that the first mode represents the basic load distribution, and the third mode reflects the periodic fluctuations of the bearing loads. This multi-dimensional force signal analysis method can comprehensively capture the dynamic characteristics of the shafting and provide accurate load distribution information for the analysis of inter-axis crosstalk.

[0122] Step 3: Perform tensor decomposition to extract the main modes. In this step, the frequency modulation features and load distribution features obtained in the above two steps are organized into a three-dimensional tensor. The three dimensions of the tensor represent the type of shaft, the position on the shaft, and the type of feature respectively. Perform Tucker decomposition on this tensor, and decompose it into the product of a core tensor and three factor matrices. Select several tensor elements with the largest contribution rate from the decomposition results, and reconstruct to obtain the main modes of shafting coupling. These main modes represent the internal correlation between frequency characteristics and load distribution. Based on the time response curves of the main modes, calculate the response time differences between different shafts. For example, perform cross-correlation analysis on the main mode time series of the high-pressure shaft and the low-pressure shaft to find the peak position of the cross-correlation function, and the time difference corresponding to this position is the response time difference between the two shafts. This multi-modal analysis method based on tensor decomposition effectively integrates vibration characteristics and load characteristics, and can capture the coupling relationship between different shaft systems.

[0123] Step 4: Perform signal transformation to calculate the amplitude ratio. In this step, perform Hilbert-Huang transform on the main modes of shafting coupling. First, perform empirical mode decomposition on the signal to obtain intrinsic mode functions, and then perform Hilbert transform on each mode function to calculate the instantaneous frequency and instantaneous amplitude. Extract the shafting natural frequency characteristics (such as 80 Hz of the low-pressure shaft and 150 Hz of the high-pressure shaft) and load frequency characteristics (such as 45 Hz of the bearing and 320 Hz of the blade) as instantaneous frequency characteristics from the transformation results, and extract the shafting amplitude characteristics and load amplitude characteristics as instantaneous amplitude characteristics. Calculate the amplitude ratio according to the instantaneous amplitude ratio at the corresponding frequencies of adjacent shafts. For example, at a frequency of 120 Hz, the amplitude ratio of the high-pressure shaft to the low-pressure shaft is 1.8:1, indicating that the transmission intensity of the torque signal corresponding to the high-pressure shaft is 1.8 times that of the low-pressure shaft at this frequency. This time-frequency analysis method based on Hilbert-Huang transform can accurately characterize the energy transfer relationship between different shaft systems and provide a quantitative basis for inter-shaft crosstalk analysis.

[0124] Step 5: Analyze the inter-shaft crosstalk characteristics. In this step, perform joint analysis on the response time difference and the amplitude ratio using variational mode decomposition. Variational mode decomposition is a non-recursive adaptive mode analysis method that can effectively process non-linear and non-stationary signals. Set the number of modes to 3 - 5 and the bandwidth constraint to 50 Hz, and decompose to obtain a series of modes with limited bandwidth through an iterative optimization algorithm. Perform correlation operations on the analysis results and the change rules of the clearances at all levels of the shafting obtained from the engine speed sensor and the clearance sensor to obtain the inter-shaft crosstalk characteristic data. This crosstalk characteristic data comprehensively reflects the dynamic coupling relationship between different shaft systems and can effectively eliminate the influence of mutual interference in the multi-shaft system on the instantaneous torque measurement.

[0125] Please continue to refer to Figure 1, collect the pressure field information of the aero-engine, and extract the pressure field spatial distribution data and the pressure field time-domain evolution data according to the pressure field information;

[0126] In one embodiment of the present invention, the collecting the pressure field information of the aero-engine and extracting the pressure field spatial distribution data and the pressure field time-domain evolution data according to the pressure field information includes:

[0127] Obtain the pressure signals at the inlet, between stages, and at the outlet of the engine compressor, perform orthogonal decomposition of the pressure signals in the radial, circumferential, and axial directions, and calculate the pressure field gradient matrix according to the orthogonal decomposition results;

[0128] Adopt the proper orthogonal decomposition method to perform modal extraction on the pressure field gradient matrix, and reconstruct the extracted dominant modes according to the energy contribution rate to obtain the main mode spatial distribution data;

[0129] Obtain the first pressure pulsation signal of the engine turbine stator blade passage and the second pressure pulsation signal of the rotor blade passage, perform spatio-temporal evolution analysis on the pressure pulsation signals by using the dynamic mode decomposition method, and extract the coherent structures of the pressure field;

[0130] Perform Kriging interpolation operation on the main mode spatial distribution data to obtain the pressure field spatial distribution data;

[0131] Perform two-dimensional discrete cosine transform on the coherent structures of the pressure field to obtain the pressure field time-domain evolution data.

[0132] The following specifically describes the steps involved in the above embodiments:

[0133] The first step is to acquire and decompose the pressure signal. This step collects the pressure signal through a high-frequency pressure sensor array installed at the inlet, various clearances, and the outlet of the aero-engine compressor. The specific layout is as follows: 8 sensors are circumferentially evenly distributed at the inlet, with 3 radial layers (inner ring, middle ring, outer ring); the same arrangement is made between stages; at the outlet, there are 12 sensors circumferentially and 4 radial layers, totaling about 100 measurement points. The acquisition frequency is set at 20 kHz to ensure capturing high-frequency pressure fluctuations. The obtained pressure signal data is subjected to three-dimensional orthogonal decomposition: first, it is decomposed along the radial direction (from the inner wall to the outer wall) into a combination of basis functions and coefficients, then Fourier decomposition is performed along the circumferential direction (0° - 360°) to obtain harmonic components of each order, and finally polynomial decomposition is carried out along the axial direction (from the inlet to the outlet). The pressure gradients in each direction are calculated based on the decomposition results and organized into a pressure field gradient matrix, where each element of the matrix represents the pressure gradient vector at a specific location and specific moment. For example, during the acceleration process of a certain type of engine, the recorded radial pressure gradient at the second-stage compressor position is 2.5 kPa / cm, the circumferential gradient is 0.8 kPa / radian, and the axial gradient is 5.2 kPa / cm. This three-dimensional orthogonal decomposition method can comprehensively characterize the spatial distribution characteristics of the internal pressure field of the engine and capture local flow characteristics and large-scale structures.

[0134] The second step is to extract the dominant modes of the pressure field. This step uses the proper orthogonal decomposition method (also known as principal component analysis or Karhunen - Loève decomposition) to process the pressure field gradient matrix. First, an autocorrelation matrix is constructed, and its eigenvalues and eigenvectors are calculated. The magnitude of the eigenvalue represents the energy contribution of each mode, and the eigenvector represents the spatial structure of the corresponding mode. Sorting the eigenvalues from largest to smallest, the first few modes (usually 5 - 8) with a cumulative energy contribution rate reaching 95% are selected as the dominant modes. For example, the analysis results of a certain type of engine show that: the energy contribution rate of the first mode is 48%, showing the average distribution of the overall pressure field; the contribution rate of the second mode is 27%, showing the axial pressure gradient change; the contribution rate of the third mode is 12%, showing the first-order non-uniform distribution in the circumferential direction. These dominant modes are weighted and reconstructed according to their respective energy contribution rates to obtain the spatial distribution data of the main modes. This mode extraction method based on energy distribution can effectively reduce the data dimension, retain the large-scale structures with the most physical significance in the pressure field, and filter out small-scale noise and disturbances.

[0135] The third step is to analyze the turbine pressure pulsation signal. This step collects pressure pulsation signals using high-temperature pressure sensors installed in the turbine stator and rotor blade ducts. Sensors are placed at 12 locations in the stator duct, including the leading edge, trailing edge, pressure side, and suction side. Sensors are placed at nine locations in the rotor blade duct, including the shroud, blade basin, and blade back. The collected first pressure pulsation signal (stator) and second pressure pulsation signal (rotor) form a spatiotemporal data matrix, with rows representing different spatial locations and columns representing different time points. Dynamic mode decomposition (DMD) is applied to these matrices: first, time-shifted pairs of the data matrices are constructed, then a linear mapping operator is calculated between the two. Finally, this operator is subjected to eigendecomposition, resulting in eigenvalues (representing temporal evolution characteristics) and eigenvectors (representing spatial distribution characteristics). Key dynamic modes are identified from the decomposition results, and coherent structures in the pressure field, manifested as coherent spatiotemporal structures, are extracted. For example, in an analysis of a certain engine, a periodic pressure fluctuation structure, dominated by the blade passage frequency, was found at the turbine stator outlet. This structure induces a resonant response at the rotor blade inlet. This method based on dynamic modal decomposition can effectively identify the main dynamic structures in the pressure field and reveal the propagation law of pressure waves in the blade channel.

[0136] The fourth step is to perform Kriging interpolation to calculate the spatial distribution. This step performs Kriging interpolation on the spatial distribution data of the main mode, and expands the data of the discrete measurement points to the entire flow field space. First, a spatial variogram model is established, and the Gaussian variogram is selected as the basic model. For each spatial position point, the optimal linear unbiased estimate is obtained by solving the Kriging equation group based on the data and position of the known measurement points. The entire flow field is gridded (such as 100 points in the axial direction, 50 points in the radial direction, and 72 points in the circumferential direction), and interpolation calculations are performed on each grid point to finally obtain high-resolution pressure field spatial distribution data. For example, the analysis results of a certain type of engine show that there is a high-pressure area in the high-pressure compressor outlet area, and the pressure value is 10% higher than that of the surrounding area. The area presents a 30° circumferential fan-shaped distribution. This geostatistical-based Kriging interpolation method has optimal estimation characteristics and can generate continuous and smooth pressure field distribution maps based on limited measurement point data, providing detailed flow field background information for torque analysis.

[0137] Step 5: Perform discrete cosine transform to obtain the time-domain evolution data. This step performs a two-dimensional discrete cosine transform on the coherent structures of the pressure field, converting the spatial-domain information into a frequency-domain representation. First, the coherent structures are organized into a two-dimensional matrix according to their spatial positions, and then the two-dimensional discrete cosine transform algorithm is applied to obtain the transformed coefficient matrix. Select the top 20% of the coefficients with larger absolute values after transformation, set the remaining coefficients to zero, and then perform an inverse transform to obtain the compressed time-domain evolution data of the pressure field. For example, the analysis results of a certain type of engine show that during the acceleration process, the pressure wave in the turbine propagates from the stator blade to the rotor blade at a speed of 17 m / s, and the amplitude decays by about 25% during the propagation. This data compression method based on discrete cosine transform can not only retain the main characteristics of the time-domain evolution of the pressure field but also effectively reduce the data volume, facilitating subsequent instantaneous torque calculation.

[0138] In one embodiment of the present invention, for obtaining the first pressure pulsation signal of the stator blade passage and the second pressure pulsation signal of the rotor blade passage of the engine turbine, performing spatio-temporal evolution analysis on the pressure pulsation signals by using the dynamic mode decomposition method, and extracting the coherent structures of the pressure field, includes:

[0139] Obtain the first pressure pulsation signals of the leading edge, trailing edge, pressure surface, and suction surface of the stator blades of the high-pressure stage and low-pressure stage of the turbine, calculate the pressure distribution matrix of the stator blade passage according to the first pressure pulsation signals, and perform singular spectrum decomposition on the pressure distribution matrix of the stator blade passage to obtain the pressure characteristic modes of the stator blade passage;

[0140] Obtain the second pressure pulsation signals of the blade crown, blade basin, and blade back of the rotor blades of the high-pressure stage and low-pressure stage of the turbine, calculate the pressure distribution matrix of the rotor blade passage according to the second pressure pulsation signals, and perform singular spectrum decomposition on the pressure distribution matrix of the rotor blade passage to obtain the pressure characteristic modes of the rotor blade passage;

[0141] Perform a non-linear superposition operation on the pressure characteristic modes of the stator blade passage and the pressure characteristic modes of the rotor blade passage according to the variation law of the turbine inter-stage gap to obtain the pressure coupling coefficient of the cascade passage;

[0142] Perform time-series analysis on the pressure coupling coefficient of the cascade passage by using the dynamic mode decomposition method, and perform aerodynamic compensation according to the variation relationship between the inlet air flow angle and the outlet air flow angle of the turbine to obtain the coherent structures of the pressure field.

[0143] The following specifically describes the steps involved in the above embodiments:

[0144] The first step is to acquire and process the static blade pressure pulsation signal. This step collects the first pressure pulsation signal through high-temperature pressure sensors installed at key positions on the static blades of the high-pressure and low-pressure stages of the turbine. The specific arrangement is as follows: One pressure sensor is installed at the leading edge (the very front of the windward side), trailing edge (the rear edge where the airflows out), pressure surface (the convex surface, the side with higher airflow pressure), and suction surface (the concave surface, the side with lower airflow pressure) of each static blade. A total of 8 points are collected for each of the high-pressure and low-pressure stages, resulting in 16 measurement points in total. The acquisition frequency is set at 40 kHz, and the sampling duration is 10 seconds to ensure capturing a complete pressure pulsation cycle. Based on the collected first pressure pulsation signal, a static blade passage pressure distribution matrix is constructed. The rows of the matrix represent different spatial positions, and the columns represent different time points. Singular spectrum decomposition is performed on this matrix to calculate the singular values and the corresponding left and right singular vectors. The magnitude of the singular values represents the energy contribution of each mode, the left singular vector represents the spatial mode, and the right singular vector represents the time evolution characteristics. The modes corresponding to the first 3 - 5 singular values are selected as the static blade passage pressure characteristic modes. For example, in the high-pressure turbine of a certain type of engine, the first characteristic mode shows the average distribution of the overall pressure, with an energy proportion of 65%; the second characteristic mode shows the pressure difference distribution between the pressure surface and the suction surface, with an energy proportion of 22%; the third characteristic mode shows the pressure fluctuation between the leading edge and the trailing edge, with an energy proportion of 8%. This method based on singular spectrum decomposition can effectively extract the most physically meaningful pressure distribution modes in the static blade passage, reflecting the characteristics of blade loading and airflow distribution.

[0145] The second step is to acquire and process the rotating blade pressure pulsation signal. This step collects the second pressure pulsation signal through high-temperature pressure sensors installed at key positions on the rotating blades of the high-pressure and low-pressure stages of the turbine. The specific arrangement is as follows: One pressure sensor is installed at the blade tip (the outermost part of the blade opposite to the casing), blade concave surface (the concave surface, the main surface impacted by the airflow), and blade convex surface (the convex surface, the surface where the airflow flows out) of each rotating blade. A total of 6 points are collected for each of the high-pressure and low-pressure stages, resulting in 12 measurement points in total. The acquisition frequency is also set at 40 kHz, and the acquisition is synchronized with the static blade signal. Based on the collected second pressure pulsation signal, a rotating blade passage pressure distribution matrix is constructed. The matrix structure is similar to that of the static blade. Singular spectrum decomposition is also performed on this matrix to extract the rotating blade passage pressure characteristic modes. For example, in the analysis of the rotating blades of the same engine, the first characteristic mode shows the pressure distribution in the blade height direction, with an energy proportion of 58%; the second characteristic mode shows the pressure difference distribution between the blade concave surface and the blade convex surface, with an energy proportion of 25%; the third characteristic mode shows the pressure pulsation near the blade tip, with an energy proportion of 10%. This pressure analysis of the key parts of the rotating blade reveals the unsteady aerodynamic load distribution borne by the rotating blade during high-speed rotation, which helps to understand the dynamic characteristics during the torque transmission process.

[0146] Step 3: Calculate the pressure coupling coefficient of the cascade channel. In this step, the variation law of the turbine inter-stage clearance is first obtained through a rotational speed sensor and a clearance measurement system. During the operation of the engine, the clearance changes with factors such as rotational speed and temperature. For example, during the startup phase, the clearance gradually decreases from 0.8 mm in the cold state to 0.5 mm in the hot state. According to this variation law, a non-linear superposition operation is performed on the pressure characteristic pattern of the stator blade channel and the pressure characteristic pattern of the rotor blade channel. The specific operation is as follows: The two characteristic patterns are related according to the spatial position mapping relationship. Considering the transfer characteristics of the airflow between the stator blade and the rotor blade, a time delay factor is introduced, corresponding to the time required for the airflow to flow from the stator blade to the rotor blade (usually 0.2 - 0.5 milliseconds). The non-linear superposition is performed using the polynomial weighting method, and the weighting coefficient is dynamically adjusted with the change of the inter-stage clearance. The smaller the clearance, the greater the coupling strength. Through this processing, the pressure coupling coefficient of the cascade channel is obtained, which characterizes the aerodynamic coupling strength between the stator blade and the rotor blade. This non-linear superposition method considering the dynamic change of the inter-stage clearance can accurately reflect the complex aerodynamic interference effect between the stator blade and the rotor blade and capture the key characteristics in the torque transfer process.

[0147] Step 4: Perform dynamic mode decomposition analysis to obtain the coherent structure of the pressure field. In this step, the dynamic mode decomposition method is used to perform time series analysis on the pressure coupling coefficient of the cascade channel. First, a time evolution matrix is constructed. The coefficients are arranged in chronological order, and each column represents the state at a certain moment. The mapping relationship between the states at the previous and subsequent moments is established, the optimal linear mapping matrix is calculated, and the eigenvalue (representing the time evolution characteristic) and eigenvector (representing the spatial distribution characteristic) are obtained by performing eigenvalue decomposition on this matrix. At the same time, the variation relationship between the inlet airflow angle (the angle with the axial direction, usually 20° - 40°) and the outlet airflow angle (usually 60° - 80°) of the turbine is obtained through the flow field measurement system. According to the change of the airflow angle, the aerodynamic correction coefficient is calculated to compensate the amplitude and phase of the dynamic mode. Finally, the aerodynamically compensated coherent structure of the pressure field is obtained, which characterizes the large-scale dynamic characteristics with physical relevance in the pressure field. For example, in the analysis of a certain type of engine, it is found that during the acceleration process, there is a pressure wave structure mainly dominated by the blade passing frequency and propagating along the flow direction. This structure starts from the stator blade of the high-pressure stage, passes through the rotor blade, the stator blade of the low-pressure stage, and finally reaches the rotor blade of the low-pressure stage, showing obvious cascading propagation characteristics. This dynamic analysis method can reveal the spatio-temporal evolution law of the pressure field and provide an important basis for understanding the torque transfer mechanism in complex flow fields.

[0148] Please continue to refer to Figure 1 , and calculate the instantaneous torque value of the engine based on the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the inter-axis crosstalk characteristic data, the pressure field spatial distribution data, and the pressure field time-domain evolution data.

[0149] In one embodiment of the present invention, calculating the engine instantaneous torque value based on the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the inter-axis crosstalk characteristic data, the pressure field spatial distribution data, and the pressure field time-domain evolution data includes:

[0150] Dividing the first acoustic response signal, the second acoustic response signal, and the third acoustic response signal into single-material segments, double-layer material segments, and multi-layer material segments, performing acoustic wave reflection analysis on the single-material segments, performing interface transmission analysis on the double-layer material segments, performing diffraction characteristic analysis on the multi-layer material segments, and performing acoustic compensation in combination with the material acoustic impedance coefficients of each stage of the shafting to obtain acoustic characteristic weight coefficients;

[0151] Performing covariance matrix decomposition based on the phase point torque response signal and the inter-axis crosstalk characteristic data, and performing energy compensation in combination with the compressor efficiency characteristic and the turbine efficiency characteristic of the engine to obtain dynamic characteristic weight coefficients;

[0152] Performing subspace mapping analysis based on the pressure field spatial distribution data and the pressure field time-domain evolution data, and performing flow field compensation in combination with the aerodynamic load coefficients of each stage of the turbine and the blade profile parameters of each stage of the compressor to obtain aerodynamic characteristic weight coefficients;

[0153] Constructing a tensor set from the acoustic characteristic weight coefficients, the dynamic characteristic weight coefficients, and the aerodynamic characteristic weight coefficients, performing feature fusion using a deep sparse autoencoder, and performing adaptive correction in combination with the engine speed and power characteristics to obtain a fusion weight matrix;

[0154] Performing weighted fusion operations on the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the inter-axis crosstalk characteristic data, the pressure field spatial distribution data, and the pressure field time-domain evolution data according to the fusion weight matrix to obtain the engine instantaneous torque value.

[0155] The following specifically describes the steps involved in the above embodiment:

[0156] The first step is to analyze and compensate the acoustic response signals. In this step, first, using the shafting structure diagram and material distribution information, the first acoustic response signal, the second acoustic response signal, and the third acoustic response signal are divided into single-material segments (such as titanium alloy segments), double-material segments (such as superalloy and thermal insulation layer), and multi-material segments (such as multi-layer composite material segments) according to the material composition. For the single-material segments, the pulse echo method is used for acoustic wave reflection analysis. By emitting pulses and receiving the reflected echoes, the propagation speed and attenuation characteristics of the acoustic waves in the material are calculated, and the acoustic wave reflection coefficient is extracted (for example, the reflection coefficient of the titanium alloy segment is 0.15). For the double-material segments, the transmitted wave technique is used for interface transmission analysis. By measuring the amplitude ratio and phase difference of the transmitted waves, the transmission coefficient and reflection coefficient of the interface are calculated (for example, the transmission coefficient of the interface between the superalloy and the ceramic thermal insulation layer is 0.65). For the multi-material segments, the scattered wave technique is used for diffraction characteristic analysis. The intensity distribution of the scattered waves in different directions is measured, and the acoustic wave diffraction index of the material is calculated (for example, the diffraction index of the multi-layer material in the turbine region is 0.38). Combining the acoustic impedance coefficients of the materials at all levels of the shafting obtained from the material database (such as 27.3× kg / m²s for titanium alloy and 41.5× kg / m²s for nickel-based superalloy), the acoustic transfer matrix method is applied for the acoustic compensation of the entire system to obtain the acoustic characteristic weight coefficients, which reflect the intensity distribution of the acoustic response in different material segments. This segmented analysis method based on the material composition performs differential processing on the acoustic characteristics of different material structures, effectively solving the problem of different acoustic wave propagation characteristics at the interfaces of heterogeneous materials.

[0157] The second step is to process the torque response and inter-axis crosstalk data to obtain the dynamic characteristic weights. In this step, the phase point torque response signals and the inter-axis crosstalk characteristic data are organized into a data matrix, where the rows of the matrix represent different shafting positions and the columns represent different time points. The covariance matrix of this matrix is calculated, and then eigenvalue decomposition is performed to extract the main eigenvectors as the dynamic characteristic basis. The compressor efficiency characteristics (such as at the design point, the isentropic efficiency of each stage of the compressor is 84% - 89%) and the turbine efficiency characteristics (such as at the design point, the isentropic efficiency of each stage of the turbine is 88% - 92%) are obtained from the engine performance database. According to these efficiency characteristics, the energy conversion efficiency of each stage is calculated, and considering the deviation between the actual and ideal operating conditions, energy compensation is performed. The specific operation is to correct the amplitude of the dynamic characteristic basis to make it more consistent with the actual energy transfer characteristics. Finally, the dynamic characteristic weight coefficients are obtained, which characterize the transmission characteristics of the torque in different shafting segments. For example, the analysis results of a certain type of engine show that the dynamic weight of the low-pressure shaft segment is 0.32, the dynamic weight of the high-pressure shaft segment is 0.45, and the dynamic weight of the fan shaft segment is 0.23. This compensation method based on the actual efficiency characteristics can accurately reflect the energy conversion efficiency of each component of the engine and correct the torque estimation error caused by ignoring the efficiency change in traditional measurements.

[0158] In the third step, the pressure field data is processed to obtain the aerodynamic characteristic weights. In this step, the spatial distribution data and the time-domain evolution data of the pressure field are used to construct the pressure field feature space. The kernel principal component analysis method is adopted for subspace mapping analysis to map the high-dimensional pressure field data to a low-dimensional feature space and extract the main aerodynamic characteristics. The aerodynamic load coefficients of each stage of the turbine (for example, the load coefficient of the first-stage turbine blade is 1.2) and the airfoil parameters of each stage of the compressor (for example, the angle of attack of the first-stage compressor airfoil is 4° and the twist angle is 12°) are obtained from the engine aerodynamic design data. According to these parameters and combined with the computational fluid dynamics model, the aerodynamic correction coefficient under the actual working conditions is calculated to perform flow field compensation on the subspace mapping result. Finally, the aerodynamic characteristic weight coefficient is obtained, which characterizes the influence intensity of the aerodynamic characteristics on torque transmission. For example, during the acceleration process of a certain engine, the aerodynamic weight of the compressor section is 0.28, the aerodynamic weight of the combustion chamber section is 0.15, and the aerodynamic weight of the turbine section is 0.57. This compensation method considering the actual aerodynamic load can accurately characterize the contribution of aerodynamic force to the shaft torque and improve the comprehensiveness and accuracy of torque measurement.

[0159] In the fourth step, a tensor set is constructed and feature fusion is performed. In this step, the acoustic characteristic weight coefficient, the dynamic characteristic weight coefficient, and the aerodynamic characteristic weight coefficient are organized into a three-dimensional tensor set. The three dimensions of the tensor represent the feature type, the shaft position, and the time point respectively. The deep sparse autoencoder based on the TensorFlow framework is used for feature fusion. The structure of the autoencoder is a 5-layer network (input layer - encoding layer 1 - encoding layer 2 - decoding layer - output layer), and the number of neurons in each layer is 100 - 64 - 32 - 64 - 100 respectively. The L1 regularization constraint is introduced in the encoding layer to achieve the sparse representation of features. The Adam optimizer is used for training, the learning rate is set to 0.001, and the number of iterations is 5000 times. By minimizing the reconstruction error and the sparsity penalty term, the non-linear dimensionality reduction and feature extraction of the tensor set are realized. Combining the rotational speed (such as N1 = 12000 rpm, N2 = 23000 rpm) and the power characteristics (such as the output power is 15 MW) obtained from the engine control system, the adaptive correction of the fusion result is performed to obtain the fusion weight matrix. This feature fusion method based on deep learning can adaptively extract the optimal feature combination from multi-source heterogeneous data and effectively solves the problem that traditional fusion methods are difficult to process high-dimensional non-linear data.

[0160] Step 5: Weighted fusion to calculate the instantaneous torque. In this step, a fusion weight matrix is used to perform a weighted fusion operation on the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the inter-axis crosstalk characteristic data, the pressure field spatial distribution data, and the pressure field time-domain evolution data. The specific implementation is to perform weighted superposition of each signal according to the weight corresponding to it in the fusion weight matrix, consider the time delay between different signals (for example, the propagation delay between the acoustic signal and the pressure signal is about 0.5 - 2 milliseconds), and align the signal phases. The engine instantaneous torque value is obtained through the weighted superposition operation. For example, during the rapid acceleration of a certain type of engine, the instantaneous torque calculated by this method rapidly rises from an initial 2000 N·m to 6500 N·m, with a response time of 0.8 seconds. Compared with the traditional measurement method, the dynamic response error is reduced by 65%, and the measurement accuracy of the torque peak is increased by 42%. This method of multi-dimensional information fusion makes full use of information from multiple perspectives such as acoustics, dynamics, and aerodynamics, effectively overcomes the measurement error caused by the reflection of stress waves at the interface of heterogeneous materials, and improves the accuracy and reliability of the instantaneous torque measurement of aero-engines.

[0161] The above describes the method for testing the instantaneous torque of an aero-engine in an embodiment of the present invention. Next, the device for testing the instantaneous torque of an aero-engine in an embodiment of the present invention will be described. Please refer to Figure 2 , an embodiment of the device for testing the instantaneous torque of an aero-engine in an embodiment of the present invention includes:

[0162] An acoustic response module 101, configured to divide the engine shafting into a first temperature zone, a second temperature zone, and a third temperature zone with increasing temperatures, apply an acoustic excitation signal with a first frequency to the first temperature zone and obtain a first acoustic response signal, apply an acoustic excitation signal with a second frequency to the second temperature zone and obtain a second acoustic response signal, apply an acoustic excitation signal with a third frequency to the third temperature zone and obtain a third acoustic response signal, where the first frequency is greater than the second frequency, and the second frequency is greater than the third frequency;

[0163] A phase response module 102, configured to determine the engine operation phase point according to the engine speed change rate and collect the phase point torque response signal at the operation phase point;

[0164] A crosstalk analysis module 103, configured to collect the inter-axis crosstalk signals of the low-pressure shaft, high-pressure shaft, and fan shaft of the aero-engine, extract the response time difference and amplitude ratio between adjacent shafts according to the inter-axis crosstalk signals, and obtain the inter-axis crosstalk characteristic data;

[0165] A pressure field analysis module 104, configured to collect the pressure field information of the aero-engine and extract the pressure field spatial distribution data and the pressure field time-domain evolution data according to the pressure field information;

[0166] The torque calculation module 105 is configured to calculate the instantaneous torque value of the engine based on the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the inter-axis crosstalk characteristic data, the pressure field spatial distribution data, and the pressure field time-domain evolution data.

[0167] above Figure 2 The instantaneous torque test device of the aeroengine in the embodiment of the present invention is described in detail from the perspective of modular functional entities. Next, the instantaneous torque test system of the aeroengine in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0168] Figure 3 FIG. is a schematic structural diagram of an instantaneous torque test system of an aeroengine provided by an embodiment of the present invention. The instantaneous torque test system 200 of the aeroengine may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 210 (for example, one or more processors) and a memory 220, and one or more storage media 230 for storing application programs 233 or data 232 (for example, one or more mass storage device terminals). Among them, the memory 220 and the storage media 230 may be transient storage or persistent storage. The program stored in the storage media 230 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the instantaneous torque test system 200 of the aeroengine. Further, the processor 210 may be configured to communicate with the storage media 230 and execute a series of instruction operations in the storage media 230 on the instantaneous torque test system 200 of the aeroengine to implement the steps of the above-mentioned instantaneous torque test method for the aeroengine.

[0169] The instantaneous torque test system 200 of the aeroengine may further include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input / output interfaces 260, and / or one or more operating systems 231, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on. Those skilled in the art can understand that Figure 3 the shown structural diagram of the instantaneous torque test system of the aeroengine does not limit the instantaneous torque test system of the aeroengine provided by the present invention, and may include more or fewer components than shown, or combine some components, or have different component arrangements.

[0170] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the instantaneous torque testing method of the aeroengine.

[0171] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system, device, or unit can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0172] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program codes.

[0173] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structural transformation made by using the description and drawings of the present invention under the inventive concept of the present invention, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present invention.

Claims

1. A method for testing the instantaneous torque of an aeroengine, characterized in that, Including: Dividing the engine shafting into a first temperature zone, a second temperature zone, and a third temperature zone with increasing temperatures, applying an acoustic excitation signal with a first frequency to the first temperature zone and obtaining a first acoustic response signal, applying an acoustic excitation signal with a second frequency to the second temperature zone and obtaining a second acoustic response signal, applying an acoustic excitation signal with a third frequency to the third temperature zone and obtaining a third acoustic response signal, where the first frequency is greater than the second frequency, and the second frequency is greater than the third frequency; Determining the engine operating phase point according to the engine speed change rate, and collecting the phase point torque response signal at the operating phase point; Collecting the inter-axis crosstalk signals of the low-pressure shaft, high-pressure shaft, and fan shaft of the aeroengine, extracting the response time difference and amplitude ratio between adjacent shafts according to the inter-axis crosstalk signals, and obtaining the inter-axis crosstalk characteristic data; Collecting the aeroengine pressure field information, and extracting the pressure field spatial distribution data and pressure field time-domain evolution data according to the pressure field information; specifically including: obtaining the pressure signals at the inlet, between stages, and at the outlet of the engine compressor, performing orthogonal decomposition of the pressure signals in the radial, circumferential, and axial directions, and calculating the pressure field gradient matrix according to the orthogonal decomposition results; Performing modal extraction on the pressure field gradient matrix using the proper orthogonal decomposition method, reconstructing the extracted dominant modes according to the energy contribution rate to obtain the main mode spatial distribution data; obtaining the first pressure pulsation signal in the static blade passage of the engine turbine and the second pressure pulsation signal in the rotating blade passage, performing spatio-temporal evolution analysis on the pressure pulsation signals using the dynamic mode decomposition method, and extracting the coherent structure of the pressure field; performing Kriging interpolation operation on the main mode spatial distribution data to obtain the pressure field spatial distribution data; performing two-dimensional discrete cosine transform on the coherent structure of the pressure field to obtain the pressure field time-domain evolution data; Calculating the engine instantaneous torque value according to the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the inter-axis crosstalk characteristic data, the pressure field spatial distribution data, and the pressure field time-domain evolution data.

2. The instantaneous torque testing method of an aeroengine according to claim 1, wherein, The step of dividing the engine shafting into a first temperature zone, a second temperature zone, and a third temperature zone with increasing temperatures, applying an acoustic excitation signal with a first frequency to the first temperature zone and obtaining a first acoustic response signal, applying an acoustic excitation signal with a second frequency to the second temperature zone and obtaining a second acoustic response signal, applying an acoustic excitation signal with a third frequency to the third temperature zone and obtaining a third acoustic response signal includes: Dividing the single-material region from the intake end of the engine shafting to the front of the compressor into the first temperature zone, dividing the double-material region from the compressor to the front of the combustion chamber into the second temperature zone, and dividing the multi-material region from the back of the combustion chamber to the turbine into the third temperature zone; Calculate the reference frequency coefficient according to the difference between the engine idle speed and the maximum operating speed, multiply the reference frequency coefficient by the acoustic impedance of the material in the first temperature zone to obtain the acoustic wave propagation frequency attenuation coefficient in the first temperature zone, set the frequency of the acoustic excitation signal at the first frequency within the range from the lower limit value of the acoustic wave propagation frequency determined by the acoustic wave propagation frequency attenuation coefficient to 30 kHz, apply the acoustic excitation signal at the first frequency to the first temperature zone and obtain the response amplitude of the first region; Calculate the compression frequency coefficient according to the pressure ratio of the engine compressor, multiply the compression frequency coefficient by the acoustic impedance ratio of the material interface in the second temperature zone to obtain the acoustic wave propagation damping coefficient in the second temperature zone, set the frequency of the acoustic excitation signal at the second frequency within the range from the lower limit value of the acoustic wave propagation frequency determined by the acoustic wave propagation damping coefficient to 20 kHz, apply the acoustic excitation signal at the second frequency to the second temperature zone, and perform interface transfer compensation on the response signal of the second temperature zone according to the response amplitude of the first region to obtain the response amplitude of the second region; Calculate the thermoacoustic frequency coefficient according to the gas temperature before the engine turbine, multiply the thermoacoustic frequency coefficient by the acoustic impedance ratio of the multi-layer material in the third temperature zone to obtain the acoustic wave propagation speed coefficient in the third temperature zone, set the frequency of the acoustic excitation signal at the third frequency within the range from the lower limit value of the acoustic wave propagation frequency determined by the acoustic wave propagation speed coefficient to 10 kHz, apply the acoustic excitation signal at the third frequency to the third temperature zone, and obtain the response amplitude of the third region by combining the response amplitude of the first region and the response amplitude of the second region; Perform frequency compensation on the response amplitude of the first region to obtain the first acoustic response signal, perform material interface compensation on the response amplitude of the second region to obtain the second acoustic response signal, and perform multi-layer interface compensation on the response amplitude of the third region to obtain the third acoustic response signal.

3. The instantaneous torque testing method of an aeroengine according to claim 1, characterized in that Determine the engine operation phase point according to the engine speed change rate, and collect the phase point torque response signal at the operation phase point, including: Obtain the pressure ratios of each stage of the engine compressor and the expansion ratios of each stage of the turbine, calculate the inter-stage power coupling factor according to the change relationship between the pressure ratios of each stage of the compressor, calculate the inter-stage energy conversion factor according to the change relationship between the expansion ratios of each stage of the turbine, and perform an association operation on the inter-stage power coupling factor and the inter-stage energy conversion factor with the engine speed signal to obtain the compensated speed change rate; Collect the inlet guide vane angle of the compressor and the nozzle opening of the turbine, calculate the intake air flow correction coefficient according to the corresponding relationship between the guide vane angle and the intake Mach number, calculate the exhaust gas flow correction coefficient according to the corresponding relationship between the nozzle opening and the back pressure coefficient, and perform polynomial fitting on the compensated speed change rate, the intake air flow correction coefficient and the exhaust gas flow correction coefficient to obtain the aerodynamic characteristic corrected speed change rate; Obtain the temperature gradient and pressure gradient of each stage of the engine, calculate the ratio of the temperature gradients between adjacent stages and the ratio of the pressure gradients between adjacent stages, perform stress wave transfer compensation operation according to the ratio of the temperature gradients and the ratio of the pressure gradients, correct the aerodynamic characteristic corrected speed change rate, and determine the engine operation phase point; Calculate the torque sampling compensation coefficient according to the changing trends of the temperature gradient ratios and pressure gradient ratios at all levels, collect the shaft torque signal at the operating phase point, and correct the sampling signal according to the torque sampling compensation coefficient to obtain the torque response signal at the phase point.

4. The instantaneous torque test method for an aero-engine according to claim 3, wherein, Collect the inlet guide vane angle of the compressor and the nozzle opening of the turbine, and calculate the intake air flow correction coefficient according to the corresponding relationship between the guide vane angle and the intake Mach number, and calculate the exhaust gas flow correction coefficient according to the corresponding relationship between the nozzle opening and the back pressure coefficient, including: Obtain the installation angles of the stator blades and the twist angles of the rotor blades at all levels at the compressor inlet, perform wavelet transform on the installation angles and twist angles, extract the main frequency characteristics and modulation characteristics of the angle changes, and calculate the cascade passage area coefficient according to the main frequency characteristics and modulation characteristics; Collect the static pressure difference and total pressure difference before and after the rotor blades at all levels at the compressor inlet, divide the static pressure difference and total pressure difference into a root region, a middle region, and a top region in the blade height direction, use the singular value decomposition method to extract the pressure pulsation characteristics of each region, and calculate the blade load distribution coefficient according to the pressure pulsation characteristics; Perform adaptive weighted combination on the cascade passage area coefficient and the blade load distribution coefficient, perform instantaneous frequency analysis on the combination result using the Hilbert-Huang transform to obtain the inlet air flow passage characteristic coefficient, and calculate the intake Mach number according to the corresponding relationship between the inlet air flow passage characteristic coefficient and the inlet guide vane angle; Obtain the throat area of the stator blades and the outlet area of the rotor blades at all levels of the turbine, collect the clearance pressure and the circumferential air pressure at all levels of the turbine, form a characteristic matrix with the throat area, outlet area, clearance pressure, and circumferential air pressure, and use the principal component analysis method to extract the aerodynamic characteristic vectors; Calculate the flow passage convergence factor and the air flow deviation factor according to the aerodynamic characteristic vectors, perform modal separation on the flow passage convergence factor and the air flow deviation factor using the empirical mode decomposition method to obtain the exhaust gas flow passage characteristic coefficient, and calculate the back pressure coefficient according to the corresponding relationship between the exhaust gas flow passage characteristic coefficient and the turbine nozzle opening; Obtain the intake air flow correction coefficient and the exhaust gas flow correction coefficient respectively according to the non-linear mapping relationship between the intake Mach number and the back pressure coefficient.

5. The instantaneous torque test method for an aero-engine according to claim 1, characterized in that, Collect the inter-axis crosstalk signals of the low-pressure shaft, high-pressure shaft, and fan shaft of the aero-engine, extract the response time difference and amplitude ratio between adjacent shafts according to the inter-axis crosstalk signals to obtain the inter-axis crosstalk characteristic data, including: Obtain the torsional vibration signals and radial displacement signals of the low-pressure shaft, high-pressure shaft, and fan shaft, segment the torsional vibration signals according to the number of compressor stages and turbine stages, perform time-frequency decomposition on each segment of the signal using the continuous wavelet transform to obtain the frequency modulation characteristics of each level of the shaft system; Obtain the radial force signals and axial force signals at adjacent bearings, calculate the shaft system force distribution coefficient according to the radial force signals and axial force signals, and perform empirical mode decomposition on the shaft system force distribution coefficient to obtain the load distribution characteristics of each level of the shaft system; Perform tensor decomposition on the frequency modulation characteristics and the load distribution characteristics, extract the main coupled modes of the shaft system, and calculate the response time difference between adjacent shafts according to the main coupled modes of the shaft system; Perform Hilbert-Huang transform on the coupled main mode of the shafting system, extract the natural frequency characteristics and load frequency characteristics of the shafting system as instantaneous frequency characteristics, extract the amplitude characteristics of the shafting system and load amplitude characteristics as instantaneous amplitude characteristics, and calculate the amplitude ratio between adjacent shafts according to the instantaneous frequency characteristics and instantaneous amplitude characteristics; Adopt variational mode decomposition to jointly analyze the response time difference and amplitude ratio, and perform correlation operation on the analysis results with the clearance change law of each level of the shafting system to obtain the inter-axis crosstalk characteristic data.

6. The instantaneous torque testing method of an aero-engine according to claim 1, characterized in that, Obtain the first pressure pulsation signal of the static blade passage of the engine turbine and the second pressure pulsation signal of the moving blade passage, and adopt the dynamic mode decomposition method to perform spatio-temporal evolution analysis on the pressure pulsation signal, and extract the coherent structure of the pressure field, including: Obtain the first pressure pulsation signals of the leading edge, trailing edge, pressure surface and suction surface of the static blades of the high-pressure stage and low-pressure stage of the turbine, calculate the pressure distribution matrix of the static blade passage according to the first pressure pulsation signals, and perform singular spectrum decomposition on the pressure distribution matrix of the static blade passage to obtain the pressure characteristic mode of the static blade passage; Obtain the second pressure pulsation signals of the blade crowns, blade basins and blade backs of the moving blades of the high-pressure stage and low-pressure stage of the turbine, calculate the pressure distribution matrix of the moving blade passage according to the second pressure pulsation signals, and perform singular spectrum decomposition on the pressure distribution matrix of the moving blade passage to obtain the pressure characteristic mode of the moving blade passage; Perform non-linear superposition operation on the pressure characteristic mode of the static blade passage and the pressure characteristic mode of the moving blade passage according to the turbine stage-to-stage clearance change law to obtain the pressure coupling coefficient of the cascade passage; Adopt the dynamic mode decomposition method to perform time series analysis on the pressure coupling coefficient of the cascade passage, and perform aerodynamic compensation according to the change relationship between the inlet air flow angle and the outlet air flow angle of the turbine to obtain the coherent structure of the pressure field.

7. The instantaneous torque testing method for an aero-engine according to claim 1, characterized in that, The calculating the instantaneous torque value of the engine according to the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the inter-axis crosstalk characteristic data, the pressure field spatial distribution data and the pressure field time domain evolution data includes: Divide the first acoustic response signal, the second acoustic response signal and the third acoustic response signal into single material segments, double-layer material segments and multi-layer material segments, perform acoustic wave reflection analysis on the single material segments, perform interface transmission analysis on the double-layer material segments, perform diffraction characteristic analysis on the multi-layer material segments, and perform acoustic compensation in combination with the material acoustic impedance coefficients of each level of the shafting system to obtain the acoustic characteristic weight coefficients; Perform covariance matrix decomposition according to the phase point torque response signal and the inter-axis crosstalk characteristic data, and perform energy compensation in combination with the compressor efficiency characteristics and turbine efficiency characteristics of the engine to obtain the dynamic characteristic weight coefficients; Perform subspace mapping analysis according to the pressure field spatial distribution data and the pressure field time domain evolution data, and perform flow field compensation in combination with the aerodynamic load coefficients of each stage of the turbine and the blade profile parameters of each stage of the compressor to obtain the aerodynamic characteristic weight coefficients; Construct a tensor set of the acoustic characteristic weight coefficients, the dynamic characteristic weight coefficients and the aerodynamic characteristic weight coefficients, adopt a deep sparse autoencoder for feature fusion, and perform adaptive correction in combination with the engine speed and power characteristics to obtain a fusion weight matrix; Perform a weighted fusion operation on the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the inter-axis crosstalk characteristic data, the pressure field spatial distribution data, and the pressure field time-domain evolution data according to the fusion weight matrix to obtain the instantaneous torque value of the engine.

8. An instantaneous torque testing device for an aero-engine, characterized in that, Including: An acoustic response module for dividing the engine shafting into a first temperature zone, a second temperature zone, and a third temperature zone with increasing temperature, applying an acoustic excitation signal with a first frequency to the first temperature zone and obtaining a first acoustic response signal, applying an acoustic excitation signal with a second frequency to the second temperature zone and obtaining a second acoustic response signal, applying an acoustic excitation signal with a third frequency to the third temperature zone and obtaining a third acoustic response signal, where the first frequency is greater than the second frequency, and the second frequency is greater than the third frequency; A phase response module for determining the engine operating phase point according to the engine speed change rate and collecting the phase point torque response signal at the operating phase point; A crosstalk analysis module for collecting the inter-axis crosstalk signals of the low-pressure shaft, high-pressure shaft, and fan shaft of the aeroengine, extracting the response time difference and amplitude ratio between adjacent shafts according to the inter-axis crosstalk signals, and obtaining the inter-axis crosstalk characteristic data; A pressure field analysis module for collecting the pressure field information of the aeroengine and extracting the pressure field spatial distribution data and the pressure field time-domain evolution data according to the pressure field information; specifically including: obtaining the pressure signals at the inlet, between stages, and at the outlet of the engine compressor, performing orthogonal decomposition of the pressure signals in the radial, circumferential, and axial directions, and calculating the pressure field gradient matrix according to the orthogonal decomposition results; using the proper orthogonal decomposition method to perform modal extraction on the pressure field gradient matrix, and reconstructing the extracted dominant modes according to the energy contribution rate to obtain the main mode spatial distribution data; obtaining the first pressure pulsation signal of the engine turbine stator blade channel and the second pressure pulsation signal of the rotor blade channel, performing spatio-temporal evolution analysis on the pressure pulsation signals using the dynamic mode decomposition method, and extracting the coherent structure of the pressure field; performing Kriging interpolation operation on the main mode spatial distribution data to obtain the pressure field spatial distribution data; performing two-dimensional discrete cosine transform on the coherent structure of the pressure field to obtain the pressure field time-domain evolution data; A torque calculation module for calculating the instantaneous torque value of the engine according to the first acoustic response signal, the second acoustic response signal, the third acoustic response signal, the phase point torque response signal, the inter-axis crosstalk characteristic data, the pressure field spatial distribution data, and the pressure field time-domain evolution data.

9. An instantaneous torque test system for an aeroengine, characterized in that, The instantaneous torque test system of the aeroengine includes: a memory and at least one processor, and instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the instantaneous torque test system of the aeroengine executes the steps of the instantaneous torque test method of the aeroengine as described in any one of claims 1-7.

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